Overview

Brought to you by YData

Dataset statistics

Number of variables46
Number of observations5347
Missing cells3815
Missing cells (%)1.6%
Duplicate rows209
Duplicate rows (%)3.9%
Total size in memory1.9 MiB
Average record size in memory368.0 B

Variable types

Categorical25
DateTime1
Numeric19
Text1

Alerts

status has constant value "ACTIVO" Constant
status_2 has constant value "ACTIVO" Constant
clase_sop22 has constant value "J3" Constant
clas_sop32 has constant value "K1" Constant
Dataset has 209 (3.9%) duplicate rowsDuplicates
canal2 is highly overall correlated with canal_sap2 and 12 other fieldsHigh correlation
canal_sap2 is highly overall correlated with canal2 and 14 other fieldsHigh correlation
cantidad is highly overall correlated with clasificacion2 and 5 other fieldsHigh correlation
categoria2 is highly overall correlated with canal2 and 16 other fieldsHigh correlation
categoria_22 is highly overall correlated with canal2 and 16 other fieldsHigh correlation
centro_costo_cuenta_clave2 is highly overall correlated with canal2 and 11 other fieldsHigh correlation
clase_sop2 is highly overall correlated with canal2 and 16 other fieldsHigh correlation
clasificacion2 is highly overall correlated with canal2 and 19 other fieldsHigh correlation
codigo_activo2 is highly overall correlated with canal2 and 14 other fieldsHigh correlation
codigo_producto2 is highly overall correlated with canal2 and 14 other fieldsHigh correlation
costo_total is highly overall correlated with cantidad and 13 other fieldsHigh correlation
cuenta_clave2 is highly overall correlated with canal2 and 11 other fieldsHigh correlation
descripcion_producto2 is highly overall correlated with canal2 and 14 other fieldsHigh correlation
descripcion_tipo_factura2 is highly overall correlated with id_tipo_factura2High correlation
ee_comercio is highly overall correlated with imacec_comercio and 2 other fieldsHigh correlation
es_festivo is highly overall correlated with tavg and 1 other fieldsHigh correlation
estimado is highly overall correlated with canal2 and 19 other fieldsHigh correlation
grado2 is highly overall correlated with canal2 and 16 other fieldsHigh correlation
icc is highly overall correlated with imce_comercio and 2 other fieldsHigh correlation
id_tipo_factura2 is highly overall correlated with clasificacion2 and 6 other fieldsHigh correlation
imacec_comercio is highly overall correlated with ee_comercio and 6 other fieldsHigh correlation
imacec_general is highly overall correlated with ee_comercio and 2 other fieldsHigh correlation
imacec_no_minero is highly overall correlated with ee_comercio and 2 other fieldsHigh correlation
imce_comercio is highly overall correlated with icc and 2 other fieldsHigh correlation
imce_general is highly overall correlated with icc and 2 other fieldsHigh correlation
ine_alimentos is highly overall correlated with ine_supermercadosHigh correlation
ine_supermercados is highly overall correlated with ine_alimentosHigh correlation
linea2 is highly overall correlated with canal_sap2 and 17 other fieldsHigh correlation
marca2 is highly overall correlated with canal2 and 16 other fieldsHigh correlation
pib is highly overall correlated with tavg and 1 other fieldsHigh correlation
stock_disponible_total is highly overall correlated with clasificacion2 and 1 other fieldsHigh correlation
tavg is highly overall correlated with es_festivo and 5 other fieldsHigh correlation
tipo_mat2 is highly overall correlated with canal_sap2 and 16 other fieldsHigh correlation
tmax is highly overall correlated with es_festivo and 4 other fieldsHigh correlation
tmin is highly overall correlated with imacec_comercio and 3 other fieldsHigh correlation
tpm is highly overall correlated with icc and 2 other fieldsHigh correlation
venta_total_neto is highly overall correlated with cantidad and 10 other fieldsHigh correlation
wspd is highly overall correlated with imacec_comercio and 4 other fieldsHigh correlation
codigo_producto2 is highly imbalanced (90.9%) Imbalance
id_tipo_factura2 is highly imbalanced (87.6%) Imbalance
descripcion_tipo_factura2 is highly imbalanced (60.3%) Imbalance
grado2 is highly imbalanced (85.4%) Imbalance
estimado is highly imbalanced (70.6%) Imbalance
clase_sop2 is highly imbalanced (82.6%) Imbalance
descripcion_producto2 is highly imbalanced (91.0%) Imbalance
clasificacion2 is highly imbalanced (70.6%) Imbalance
linea2 is highly imbalanced (78.6%) Imbalance
categoria2 is highly imbalanced (82.3%) Imbalance
marca2 is highly imbalanced (82.3%) Imbalance
categoria_22 is highly imbalanced (85.4%) Imbalance
codigo_activo2 is highly imbalanced (90.9%) Imbalance
tipo_mat2 is highly imbalanced (79.5%) Imbalance
cyber_monday is highly imbalanced (96.3%) Imbalance
black_friday is highly imbalanced (96.3%) Imbalance
es_festivo is highly imbalanced (67.7%) Imbalance
descripcion_tipo_factura2 has 650 (12.2%) missing values Missing
numero_documento2 has 2533 (47.4%) missing values Missing
clase_sop22 has 277 (5.2%) missing values Missing
clas_sop32 has 277 (5.2%) missing values Missing
stock_disponible_total has 5235 (97.9%) zeros Zeros
cantidad has 2563 (47.9%) zeros Zeros
venta_total_neto has 2535 (47.4%) zeros Zeros
costo_total has 2565 (48.0%) zeros Zeros

Reproduction

Analysis started2024-12-11 01:06:31.456725
Analysis finished2024-12-11 01:06:51.377654
Duration19.92 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

codigo_producto2
Categorical

High correlation  Imbalance 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
A334
5070 
A183
 
116
A344
 
58
A437
 
18
A363
 
18
Other values (25)
 
67

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21388
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.4%

Sample

1st rowA183
2nd rowA183
3rd rowA183
4th rowA183
5th rowA183

Common Values

ValueCountFrequency (%)
A334 5070
94.8%
A183 116
 
2.2%
A344 58
 
1.1%
A437 18
 
0.3%
A363 18
 
0.3%
A361 15
 
0.3%
A362 14
 
0.3%
A448 9
 
0.2%
A430 5
 
0.1%
A432 3
 
0.1%
Other values (20) 21
 
0.4%

Length

2024-12-10T22:06:51.408756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a334 5070
94.8%
a183 116
 
2.2%
a344 58
 
1.1%
a437 18
 
0.3%
a363 18
 
0.3%
a361 15
 
0.3%
a362 14
 
0.3%
a448 9
 
0.2%
a430 5
 
0.1%
a432 3
 
0.1%
Other values (20) 21
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 10413
48.7%
A 5347
25.0%
4 5246
24.5%
1 139
 
0.6%
8 129
 
0.6%
6 50
 
0.2%
2 24
 
0.1%
7 21
 
0.1%
0 13
 
0.1%
5 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 10413
48.7%
A 5347
25.0%
4 5246
24.5%
1 139
 
0.6%
8 129
 
0.6%
6 50
 
0.2%
2 24
 
0.1%
7 21
 
0.1%
0 13
 
0.1%
5 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 10413
48.7%
A 5347
25.0%
4 5246
24.5%
1 139
 
0.6%
8 129
 
0.6%
6 50
 
0.2%
2 24
 
0.1%
7 21
 
0.1%
0 13
 
0.1%
5 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 10413
48.7%
A 5347
25.0%
4 5246
24.5%
1 139
 
0.6%
8 129
 
0.6%
6 50
 
0.2%
2 24
 
0.1%
7 21
 
0.1%
0 13
 
0.1%
5 6
 
< 0.1%

fecha
Date

Distinct543
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
Minimum2021-02-01 00:00:00
Maximum2024-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-10T22:06:51.460085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:51.521625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stock_disponible_total
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5636621.4
Minimum0
Maximum7.44562 × 108
Zeros5235
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:51.648024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.44562 × 108
Range7.44562 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45441388
Coefficient of variation (CV)8.061813
Kurtosis90.326389
Mean5636621.4
Median Absolute Deviation (MAD)0
Skewness9.1185688
Sum3.0139015 × 1010
Variance2.0649197 × 1015
MonotonicityNot monotonic
2024-12-10T22:06:51.730390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 5235
97.9%
10395000 10
 
0.2%
364305000 10
 
0.2%
212977000 7
 
0.1%
539750000 6
 
0.1%
535628320 5
 
0.1%
78467000 5
 
0.1%
16531000 3
 
0.1%
26210000 2
 
< 0.1%
236054000 2
 
< 0.1%
Other values (39) 62
 
1.2%
ValueCountFrequency (%)
0 5235
97.9%
295300 1
 
< 0.1%
10395000 10
 
0.2%
10539000 1
 
< 0.1%
14173000 1
 
< 0.1%
16531000 3
 
0.1%
26210000 2
 
< 0.1%
78467000 5
 
0.1%
138006000 1
 
< 0.1%
149141000 1
 
< 0.1%
ValueCountFrequency (%)
744562000 1
 
< 0.1%
539750000 6
0.1%
535628320 5
0.1%
525718000 2
 
< 0.1%
477361000 2
 
< 0.1%
475624000 2
 
< 0.1%
446090000 2
 
< 0.1%
436110680 1
 
< 0.1%
392507000 2
 
< 0.1%
384186000 2
 
< 0.1%

cantidad
Real number (ℝ)

High correlation  Zeros 

Distinct274
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean922695.97
Minimum-30960000
Maximum30960000
Zeros2563
Zeros (%)47.9%
Negative156
Negative (%)2.9%
Memory size41.9 KiB
2024-12-10T22:06:51.850332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-30960000
5-th percentile0
Q10
median0
Q3150000
95-th percentile2000000
Maximum30960000
Range61920000
Interquartile range (IQR)150000

Descriptive statistics

Standard deviation4841929.6
Coefficient of variation (CV)5.2475894
Kurtosis27.398553
Mean922695.97
Median Absolute Deviation (MAD)10000
Skewness4.4989082
Sum4.9336554 × 109
Variance2.3444282 × 1013
MonotonicityNot monotonic
2024-12-10T22:06:51.908432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2563
47.9%
100000 418
 
7.8%
200000 310
 
5.8%
50000 259
 
4.8%
300000 160
 
3.0%
150000 155
 
2.9%
20000 145
 
2.7%
1000000 144
 
2.7%
30000 126
 
2.4%
500000 92
 
1.7%
Other values (264) 975
 
18.2%
ValueCountFrequency (%)
-30960000 1
 
< 0.1%
-28830000 2
< 0.1%
-28660000 1
 
< 0.1%
-28590000 1
 
< 0.1%
-28000000 3
0.1%
-27740000 1
 
< 0.1%
-26000000 1
 
< 0.1%
-25010000 1
 
< 0.1%
-10000000 1
 
< 0.1%
-3300000 1
 
< 0.1%
ValueCountFrequency (%)
30960000 1
< 0.1%
30120000 1
< 0.1%
29550000 1
< 0.1%
29530000 1
< 0.1%
29510000 1
< 0.1%
29420000 1
< 0.1%
29320000 1
< 0.1%
29290000 1
< 0.1%
29250000 1
< 0.1%
29210000 2
< 0.1%

venta_total_neto
Real number (ℝ)

High correlation  Zeros 

Distinct565
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2164804 × 1019
Minimum-1.123848 × 1021
Maximum1.6704 × 1021
Zeros2535
Zeros (%)47.4%
Negative179
Negative (%)3.3%
Memory size41.9 KiB
2024-12-10T22:06:51.963662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.123848 × 1021
5-th percentile0
Q10
median0
Q31.29525 × 1019
95-th percentile1.758 × 1020
Maximum1.6704 × 1021
Range2.794248 × 1021
Interquartile range (IQR)1.29525 × 1019

Descriptive statistics

Standard deviation1.7748422 × 1020
Coefficient of variation (CV)4.2092979
Kurtosis27.371124
Mean4.2164804 × 1019
Median Absolute Deviation (MAD)1.025 × 1018
Skewness4.3535181
Sum2.254552 × 1023
Variance3.1500649 × 1040
MonotonicityNot monotonic
2024-12-10T22:06:52.018449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2535
47.4%
8.64 × 1018225
 
4.2%
1.728 × 1019147
 
2.7%
4.32 × 1018131
 
2.4%
1.025 × 1019109
 
2.0%
2.05 × 101993
 
1.7%
1.296 × 101970
 
1.3%
1.728 × 101866
 
1.2%
5.125 × 101866
 
1.2%
2.592 × 101965
 
1.2%
Other values (555) 1840
34.4%
ValueCountFrequency (%)
-1.123848 × 10211
 
< 0.1%
-9.8022 × 10202
< 0.1%
-9.7444 × 10201
 
< 0.1%
-9.7206 × 10201
 
< 0.1%
-9.62 × 10201
 
< 0.1%
-9.52 × 10203
0.1%
-9.4316 × 10201
 
< 0.1%
-8.5034 × 10201
 
< 0.1%
-6.105 × 10201
 
< 0.1%
-3.5 × 10201
 
< 0.1%
ValueCountFrequency (%)
1.6704 × 10211
 
< 0.1%
1.669008 × 10211
 
< 0.1%
1.66054 × 10211
 
< 0.1%
1.6416 × 10214
0.1%
1.4196 × 10211
 
< 0.1%
1.2768 × 10211
 
< 0.1%
1.123848 × 10211
 
< 0.1%
1.118 × 10211
 
< 0.1%
1.108416 × 10211
 
< 0.1%
1.082367 × 10211
 
< 0.1%

costo_total
Real number (ℝ)

High correlation  Zeros 

Distinct819
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0150122 × 1019
Minimum-1.3390649 × 1021
Maximum3.1154839 × 1021
Zeros2565
Zeros (%)48.0%
Negative161
Negative (%)3.0%
Memory size41.9 KiB
2024-12-10T22:06:52.088365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.3390649 × 1021
5-th percentile0
Q10
median0
Q39.3460982 × 1018
95-th percentile1.3548299 × 1020
Maximum3.1154839 × 1021
Range4.4545488 × 1021
Interquartile range (IQR)9.3460982 × 1018

Descriptive statistics

Standard deviation1.4473495 × 1020
Coefficient of variation (CV)4.8004765
Kurtosis100.62245
Mean3.0150122 × 1019
Median Absolute Deviation (MAD)6.9130289 × 1017
Skewness7.3006018
Sum1.612127 × 1023
Variance2.0948207 × 1040
MonotonicityNot monotonic
2024-12-10T22:06:52.914244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2565
48.0%
5.961039062 × 101834
 
0.6%
6.378937135 × 101832
 
0.6%
5.968851689 × 101827
 
0.5%
7.521857033 × 101824
 
0.4%
6.10924174 × 101824
 
0.4%
6.361829068 × 101823
 
0.4%
6.361219111 × 101823
 
0.4%
6.362481535 × 101822
 
0.4%
1.192207812 × 101922
 
0.4%
Other values (809) 2551
47.7%
ValueCountFrequency (%)
-1.339064949 × 10211
< 0.1%
-1.193064 × 10211
< 0.1%
-1.01752 × 10212
< 0.1%
-1.0158413 × 10211
< 0.1%
-6.211670629 × 10201
< 0.1%
-5.982173952 × 10201
< 0.1%
-5.768343021 × 10201
< 0.1%
-5.372641293 × 10201
< 0.1%
-5.264553012 × 10201
< 0.1%
-4.487892728 × 10201
< 0.1%
ValueCountFrequency (%)
3.1154839 × 10211
 
< 0.1%
1.96401373 × 10215
0.1%
1.962376678 × 10211
 
< 0.1%
1.952420222 × 10211
 
< 0.1%
1.4882601 × 10211
 
< 0.1%
1.3419492 × 10211
 
< 0.1%
1.339064949 × 10212
 
< 0.1%
1.2944182 × 10211
 
< 0.1%
1.239579083 × 10211
 
< 0.1%
1.193063973 × 10211
 
< 0.1%

id_tipo_factura2
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size41.9 KiB
B1
5058 
B5
 
145
B24
 
83
B8
 
16
B25
 
14
Other values (5)
 
30

Length

Max length3
Median length2
Mean length2.0183315
Min length2

Characters and Unicode

Total characters10790
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB1
2nd rowB24
3rd rowB24
4th rowB24
5th rowB24

Common Values

ValueCountFrequency (%)
B1 5058
94.6%
B5 145
 
2.7%
B24 83
 
1.6%
B8 16
 
0.3%
B25 14
 
0.3%
B6 11
 
0.2%
B4 7
 
0.1%
B7 6
 
0.1%
B9 5
 
0.1%
B33 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2024-12-10T22:06:52.969082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:53.021593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
b1 5058
94.6%
b5 145
 
2.7%
b24 83
 
1.6%
b8 16
 
0.3%
b25 14
 
0.3%
b6 11
 
0.2%
b4 7
 
0.1%
b7 6
 
0.1%
b9 5
 
0.1%
b33 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B 5346
49.5%
1 5058
46.9%
5 159
 
1.5%
2 97
 
0.9%
4 90
 
0.8%
8 16
 
0.1%
6 11
 
0.1%
7 6
 
0.1%
9 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5346
49.5%
1 5058
46.9%
5 159
 
1.5%
2 97
 
0.9%
4 90
 
0.8%
8 16
 
0.1%
6 11
 
0.1%
7 6
 
0.1%
9 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5346
49.5%
1 5058
46.9%
5 159
 
1.5%
2 97
 
0.9%
4 90
 
0.8%
8 16
 
0.1%
6 11
 
0.1%
7 6
 
0.1%
9 5
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5346
49.5%
1 5058
46.9%
5 159
 
1.5%
2 97
 
0.9%
4 90
 
0.8%
8 16
 
0.1%
6 11
 
0.1%
7 6
 
0.1%
9 5
 
< 0.1%
3 2
 
< 0.1%

descripcion_tipo_factura2
Categorical

High correlation  Imbalance  Missing 

Distinct10
Distinct (%)0.2%
Missing650
Missing (%)12.2%
Memory size41.9 KiB
C2
2382 
C1
2090 
C4
 
138
C6
 
30
C10
 
14
Other values (5)
 
43

Length

Max length3
Median length2
Mean length2.0083032
Min length2

Characters and Unicode

Total characters9433
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC1
2nd rowC1
3rd rowC1
4th rowC1
5th rowC1

Common Values

ValueCountFrequency (%)
C2 2382
44.5%
C1 2090
39.1%
C4 138
 
2.6%
C6 30
 
0.6%
C10 14
 
0.3%
C20 14
 
0.3%
C7 11
 
0.2%
C3 7
 
0.1%
C12 6
 
0.1%
C11 5
 
0.1%
(Missing) 650
 
12.2%

Length

2024-12-10T22:06:53.076279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:53.124151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
c2 2382
50.7%
c1 2090
44.5%
c4 138
 
2.9%
c6 30
 
0.6%
c10 14
 
0.3%
c20 14
 
0.3%
c7 11
 
0.2%
c3 7
 
0.1%
c12 6
 
0.1%
c11 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 4697
49.8%
2 2402
25.5%
1 2120
22.5%
4 138
 
1.5%
6 30
 
0.3%
0 28
 
0.3%
7 11
 
0.1%
3 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9433
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 4697
49.8%
2 2402
25.5%
1 2120
22.5%
4 138
 
1.5%
6 30
 
0.3%
0 28
 
0.3%
7 11
 
0.1%
3 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9433
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 4697
49.8%
2 2402
25.5%
1 2120
22.5%
4 138
 
1.5%
6 30
 
0.3%
0 28
 
0.3%
7 11
 
0.1%
3 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9433
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 4697
49.8%
2 2402
25.5%
1 2120
22.5%
4 138
 
1.5%
6 30
 
0.3%
0 28
 
0.3%
7 11
 
0.1%
3 7
 
0.1%

numero_documento2
Text

Missing 

Distinct1960
Distinct (%)69.7%
Missing2533
Missing (%)47.4%
Memory size41.9 KiB
2024-12-10T22:06:53.213907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.2391613
Min length1

Characters and Unicode

Total characters14743
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1947 ?
Unique (%)69.2%

Sample

1st rowD1
2nd rowD204015
3rd rowD204016
4th rowD204058
5th rowD204060
ValueCountFrequency (%)
1 825
29.3%
d168329 8
 
0.3%
d213567 7
 
0.2%
d216837 6
 
0.2%
d216831 3
 
0.1%
d193631 3
 
0.1%
d213743 3
 
0.1%
d248380 2
 
0.1%
d242133 2
 
0.1%
d224845 2
 
0.1%
Other values (1950) 1953
69.4%
2024-12-10T22:06:53.566031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2996
20.3%
1 2000
13.6%
D 1989
13.5%
3 1223
8.3%
4 1213
8.2%
5 1107
 
7.5%
0 999
 
6.8%
7 892
 
6.1%
6 844
 
5.7%
9 751
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2996
20.3%
1 2000
13.6%
D 1989
13.5%
3 1223
8.3%
4 1213
8.2%
5 1107
 
7.5%
0 999
 
6.8%
7 892
 
6.1%
6 844
 
5.7%
9 751
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2996
20.3%
1 2000
13.6%
D 1989
13.5%
3 1223
8.3%
4 1213
8.2%
5 1107
 
7.5%
0 999
 
6.8%
7 892
 
6.1%
6 844
 
5.7%
9 751
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2996
20.3%
1 2000
13.6%
D 1989
13.5%
3 1223
8.3%
4 1213
8.2%
5 1107
 
7.5%
0 999
 
6.8%
7 892
 
6.1%
6 844
 
5.7%
9 751
 
5.1%

cuenta_clave2
Categorical

High correlation 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
I1
3307 
I2
817 
I6
705 
I4
 
220
I18
 
112
Other values (6)
 
186

Length

Max length3
Median length2
Mean length2.0389003
Min length2

Characters and Unicode

Total characters10902
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowI18
2nd rowI4
3rd rowI4
4th rowI4
5th rowI4

Common Values

ValueCountFrequency (%)
I1 3307
61.8%
I2 817
 
15.3%
I6 705
 
13.2%
I4 220
 
4.1%
I18 112
 
2.1%
I5 90
 
1.7%
I19 77
 
1.4%
I21 13
 
0.2%
I14 4
 
0.1%
I22 1
 
< 0.1%

Length

2024-12-10T22:06:53.637815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i1 3307
61.8%
i2 817
 
15.3%
i6 705
 
13.2%
i4 220
 
4.1%
i18 112
 
2.1%
i5 90
 
1.7%
i19 77
 
1.4%
i21 13
 
0.2%
i14 4
 
0.1%
i22 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

centro_costo_cuenta_clave2
Categorical

High correlation 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
J1
3307 
J2
817 
J6
705 
J4
 
220
J18
 
112
Other values (6)
 
186

Length

Max length3
Median length2
Mean length2.0389003
Min length2

Characters and Unicode

Total characters10902
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowJ18
2nd rowJ4
3rd rowJ4
4th rowJ4
5th rowJ4

Common Values

ValueCountFrequency (%)
J1 3307
61.8%
J2 817
 
15.3%
J6 705
 
13.2%
J4 220
 
4.1%
J18 112
 
2.1%
J5 90
 
1.7%
J19 77
 
1.4%
J21 13
 
0.2%
J14 4
 
0.1%
J22 1
 
< 0.1%

Length

2024-12-10T22:06:53.692259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j1 3307
61.8%
j2 817
 
15.3%
j6 705
 
13.2%
j4 220
 
4.1%
j18 112
 
2.1%
j5 90
 
1.7%
j19 77
 
1.4%
j21 13
 
0.2%
j14 4
 
0.1%
j22 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
J 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 5347
49.0%
1 3514
32.2%
2 832
 
7.6%
6 705
 
6.5%
4 224
 
2.1%
8 112
 
1.0%
5 90
 
0.8%
9 77
 
0.7%
0 1
 
< 0.1%

grado2
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
K39
5070 
K25
 
180
K31
 
29
K50
 
29
K32
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16041
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK25
2nd rowK25
3rd rowK25
4th rowK25
5th rowK25

Common Values

ValueCountFrequency (%)
K39 5070
94.8%
K25 180
 
3.4%
K31 29
 
0.5%
K50 29
 
0.5%
K32 22
 
0.4%
K29 17
 
0.3%

Length

2024-12-10T22:06:53.740066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:53.892818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
k39 5070
94.8%
k25 180
 
3.4%
k31 29
 
0.5%
k50 29
 
0.5%
k32 22
 
0.4%
k29 17
 
0.3%

Most occurring characters

ValueCountFrequency (%)
K 5347
33.3%
3 5121
31.9%
9 5087
31.7%
2 219
 
1.4%
5 209
 
1.3%
1 29
 
0.2%
0 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 5347
33.3%
3 5121
31.9%
9 5087
31.7%
2 219
 
1.4%
5 209
 
1.3%
1 29
 
0.2%
0 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 5347
33.3%
3 5121
31.9%
9 5087
31.7%
2 219
 
1.4%
5 209
 
1.3%
1 29
 
0.2%
0 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 5347
33.3%
3 5121
31.9%
9 5087
31.7%
2 219
 
1.4%
5 209
 
1.3%
1 29
 
0.2%
0 29
 
0.2%

canal2
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
L1
3392 
L2
1849 
L3
 
106

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL3
2nd rowL2
3rd rowL2
4th rowL2
5th rowL2

Common Values

ValueCountFrequency (%)
L1 3392
63.4%
L2 1849
34.6%
L3 106
 
2.0%

Length

2024-12-10T22:06:53.938009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:53.987748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
l1 3392
63.4%
l2 1849
34.6%
l3 106
 
2.0%

Most occurring characters

ValueCountFrequency (%)
L 5347
50.0%
1 3392
31.7%
2 1849
 
17.3%
3 106
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 5347
50.0%
1 3392
31.7%
2 1849
 
17.3%
3 106
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 5347
50.0%
1 3392
31.7%
2 1849
 
17.3%
3 106
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 5347
50.0%
1 3392
31.7%
2 1849
 
17.3%
3 106
 
1.0%

canal_sap2
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
M1
3392 
M2
1271 
M4
361 
M7
 
134
M5
 
106

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM5
2nd rowM7
3rd rowM7
4th rowM7
5th rowM7

Common Values

ValueCountFrequency (%)
M1 3392
63.4%
M2 1271
 
23.8%
M4 361
 
6.8%
M7 134
 
2.5%
M5 106
 
2.0%
M3 83
 
1.6%

Length

2024-12-10T22:06:54.033923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.075116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
m1 3392
63.4%
m2 1271
 
23.8%
m4 361
 
6.8%
m7 134
 
2.5%
m5 106
 
2.0%
m3 83
 
1.6%

Most occurring characters

ValueCountFrequency (%)
M 5347
50.0%
1 3392
31.7%
2 1271
 
11.9%
4 361
 
3.4%
7 134
 
1.3%
5 106
 
1.0%
3 83
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 5347
50.0%
1 3392
31.7%
2 1271
 
11.9%
4 361
 
3.4%
7 134
 
1.3%
5 106
 
1.0%
3 83
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 5347
50.0%
1 3392
31.7%
2 1271
 
11.9%
4 361
 
3.4%
7 134
 
1.3%
5 106
 
1.0%
3 83
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 5347
50.0%
1 3392
31.7%
2 1271
 
11.9%
4 361
 
3.4%
7 134
 
1.3%
5 106
 
1.0%
3 83
 
0.8%

status
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
ACTIVO
5347 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters32082
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVO
2nd rowACTIVO
3rd rowACTIVO
4th rowACTIVO
5th rowACTIVO

Common Values

ValueCountFrequency (%)
ACTIVO 5347
100.0%

Length

2024-12-10T22:06:54.120999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.158090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
activo 5347
100.0%

Most occurring characters

ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

status_2
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
ACTIVO
5347 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters32082
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVO
2nd rowACTIVO
3rd rowACTIVO
4th rowACTIVO
5th rowACTIVO

Common Values

ValueCountFrequency (%)
ACTIVO 5347
100.0%

Length

2024-12-10T22:06:54.196048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.230561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
activo 5347
100.0%

Most occurring characters

ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5347
16.7%
C 5347
16.7%
T 5347
16.7%
I 5347
16.7%
V 5347
16.7%
O 5347
16.7%

estimado
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
OK
5070 
NO
 
277

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd rowNO
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
OK 5070
94.8%
NO 277
 
5.2%

Length

2024-12-10T22:06:54.267621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.306418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ok 5070
94.8%
no 277
 
5.2%

Most occurring characters

ValueCountFrequency (%)
O 5347
50.0%
K 5070
47.4%
N 277
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 5347
50.0%
K 5070
47.4%
N 277
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 5347
50.0%
K 5070
47.4%
N 277
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 5347
50.0%
K 5070
47.4%
N 277
 
2.6%

clase_sop2
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing5
Missing (%)0.1%
Memory size41.9 KiB
I13
5070 
I1
 
179
I6
 
77
I2
 
16

Length

Max length3
Median length3
Mean length2.9490827
Min length2

Characters and Unicode

Total characters15754
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI1
2nd rowI1
3rd rowI1
4th rowI1
5th rowI1

Common Values

ValueCountFrequency (%)
I13 5070
94.8%
I1 179
 
3.3%
I6 77
 
1.4%
I2 16
 
0.3%
(Missing) 5
 
0.1%

Length

2024-12-10T22:06:54.350867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.390801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
i13 5070
94.9%
i1 179
 
3.4%
i6 77
 
1.4%
i2 16
 
0.3%

Most occurring characters

ValueCountFrequency (%)
I 5342
33.9%
1 5249
33.3%
3 5070
32.2%
6 77
 
0.5%
2 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 5342
33.9%
1 5249
33.3%
3 5070
32.2%
6 77
 
0.5%
2 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 5342
33.9%
1 5249
33.3%
3 5070
32.2%
6 77
 
0.5%
2 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 5342
33.9%
1 5249
33.3%
3 5070
32.2%
6 77
 
0.5%
2 16
 
0.1%

descripcion_producto2
Categorical

High correlation  Imbalance 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
A384
5070 
A33
 
121
A108
 
58
A432
 
18
A146
 
18
Other values (24)
 
62

Length

Max length4
Median length4
Mean length3.9747522
Min length3

Characters and Unicode

Total characters21253
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.4%

Sample

1st rowA33
2nd rowA33
3rd rowA33
4th rowA33
5th rowA33

Common Values

ValueCountFrequency (%)
A384 5070
94.8%
A33 121
 
2.3%
A108 58
 
1.1%
A432 18
 
0.3%
A146 18
 
0.3%
A143 15
 
0.3%
A144 14
 
0.3%
A443 9
 
0.2%
A128 3
 
0.1%
A64 2
 
< 0.1%
Other values (19) 19
 
0.4%

Length

2024-12-10T22:06:54.434914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a384 5070
94.8%
a33 121
 
2.3%
a108 58
 
1.1%
a432 18
 
0.3%
a146 18
 
0.3%
a143 15
 
0.3%
a144 14
 
0.3%
a443 9
 
0.2%
a128 3
 
0.1%
a64 2
 
< 0.1%
Other values (19) 19
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 5357
25.2%
A 5347
25.2%
4 5178
24.4%
8 5131
24.1%
1 118
 
0.6%
0 62
 
0.3%
2 25
 
0.1%
6 23
 
0.1%
9 8
 
< 0.1%
5 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5357
25.2%
A 5347
25.2%
4 5178
24.4%
8 5131
24.1%
1 118
 
0.6%
0 62
 
0.3%
2 25
 
0.1%
6 23
 
0.1%
9 8
 
< 0.1%
5 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5357
25.2%
A 5347
25.2%
4 5178
24.4%
8 5131
24.1%
1 118
 
0.6%
0 62
 
0.3%
2 25
 
0.1%
6 23
 
0.1%
9 8
 
< 0.1%
5 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5357
25.2%
A 5347
25.2%
4 5178
24.4%
8 5131
24.1%
1 118
 
0.6%
0 62
 
0.3%
2 25
 
0.1%
6 23
 
0.1%
9 8
 
< 0.1%
5 3
 
< 0.1%

clasificacion2
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
B2
5070 
B1
 
277

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB1
2nd rowB1
3rd rowB1
4th rowB1
5th rowB1

Common Values

ValueCountFrequency (%)
B2 5070
94.8%
B1 277
 
5.2%

Length

2024-12-10T22:06:54.484864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.533260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
b2 5070
94.8%
b1 277
 
5.2%

Most occurring characters

ValueCountFrequency (%)
B 5347
50.0%
2 5070
47.4%
1 277
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5347
50.0%
2 5070
47.4%
1 277
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5347
50.0%
2 5070
47.4%
1 277
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5347
50.0%
2 5070
47.4%
1 277
 
2.6%

linea2
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
C9
5070 
C1
 
196
C2
 
81

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC1
2nd rowC1
3rd rowC1
4th rowC1
5th rowC1

Common Values

ValueCountFrequency (%)
C9 5070
94.8%
C1 196
 
3.7%
C2 81
 
1.5%

Length

2024-12-10T22:06:54.586436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.635010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
c9 5070
94.8%
c1 196
 
3.7%
c2 81
 
1.5%

Most occurring characters

ValueCountFrequency (%)
C 5347
50.0%
9 5070
47.4%
1 196
 
1.8%
2 81
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 5347
50.0%
9 5070
47.4%
1 196
 
1.8%
2 81
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 5347
50.0%
9 5070
47.4%
1 196
 
1.8%
2 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 5347
50.0%
9 5070
47.4%
1 196
 
1.8%
2 81
 
0.8%

categoria2
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
D21
5070 
D1
 
180
D6
 
81
D2
 
16

Length

Max length3
Median length3
Mean length2.9481952
Min length2

Characters and Unicode

Total characters15764
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD1
2nd rowD1
3rd rowD1
4th rowD1
5th rowD1

Common Values

ValueCountFrequency (%)
D21 5070
94.8%
D1 180
 
3.4%
D6 81
 
1.5%
D2 16
 
0.3%

Length

2024-12-10T22:06:54.684306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:54.919124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
d21 5070
94.8%
d1 180
 
3.4%
d6 81
 
1.5%
d2 16
 
0.3%

Most occurring characters

ValueCountFrequency (%)
D 5347
33.9%
1 5250
33.3%
2 5086
32.3%
6 81
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5347
33.9%
1 5250
33.3%
2 5086
32.3%
6 81
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5347
33.9%
1 5250
33.3%
2 5086
32.3%
6 81
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5347
33.9%
1 5250
33.3%
2 5086
32.3%
6 81
 
0.5%

marca2
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
E10
5070 
E1
 
180
E6
 
81
E2
 
16

Length

Max length3
Median length3
Mean length2.9481952
Min length2

Characters and Unicode

Total characters15764
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE1
2nd rowE1
3rd rowE1
4th rowE1
5th rowE1

Common Values

ValueCountFrequency (%)
E10 5070
94.8%
E1 180
 
3.4%
E6 81
 
1.5%
E2 16
 
0.3%

Length

2024-12-10T22:06:54.971821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.019301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
e10 5070
94.8%
e1 180
 
3.4%
e6 81
 
1.5%
e2 16
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 5347
33.9%
1 5250
33.3%
0 5070
32.2%
6 81
 
0.5%
2 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 5347
33.9%
1 5250
33.3%
0 5070
32.2%
6 81
 
0.5%
2 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 5347
33.9%
1 5250
33.3%
0 5070
32.2%
6 81
 
0.5%
2 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 5347
33.9%
1 5250
33.3%
0 5070
32.2%
6 81
 
0.5%
2 16
 
0.1%

categoria_22
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
F41
5070 
F1
 
180
F10
 
29
F11
 
29
F9
 
22

Length

Max length3
Median length3
Mean length2.9590425
Min length2

Characters and Unicode

Total characters15822
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF1
2nd rowF1
3rd rowF1
4th rowF1
5th rowF1

Common Values

ValueCountFrequency (%)
F41 5070
94.8%
F1 180
 
3.4%
F10 29
 
0.5%
F11 29
 
0.5%
F9 22
 
0.4%
F4 17
 
0.3%

Length

2024-12-10T22:06:55.066303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.110271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f41 5070
94.8%
f1 180
 
3.4%
f10 29
 
0.5%
f11 29
 
0.5%
f9 22
 
0.4%
f4 17
 
0.3%

Most occurring characters

ValueCountFrequency (%)
F 5347
33.8%
1 5337
33.7%
4 5087
32.2%
0 29
 
0.2%
9 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 5347
33.8%
1 5337
33.7%
4 5087
32.2%
0 29
 
0.2%
9 22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 5347
33.8%
1 5337
33.7%
4 5087
32.2%
0 29
 
0.2%
9 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 5347
33.8%
1 5337
33.7%
4 5087
32.2%
0 29
 
0.2%
9 22
 
0.1%

codigo_activo2
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
G361
5070 
G29
 
122
G101
 
58
G403
 
18
G136
 
18
Other values (23)
 
61

Length

Max length4
Median length4
Mean length3.9741911
Min length3

Characters and Unicode

Total characters21250
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.3%

Sample

1st rowG29
2nd rowG29
3rd rowG29
4th rowG29
5th rowG29

Common Values

ValueCountFrequency (%)
G361 5070
94.8%
G29 122
 
2.3%
G101 58
 
1.1%
G403 18
 
0.3%
G136 18
 
0.3%
G133 15
 
0.3%
G134 14
 
0.3%
G414 9
 
0.2%
G118 3
 
0.1%
G58 2
 
< 0.1%
Other values (18) 18
 
0.3%

Length

2024-12-10T22:06:55.165113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g361 5070
94.8%
g29 122
 
2.3%
g101 58
 
1.1%
g403 18
 
0.3%
g136 18
 
0.3%
g133 15
 
0.3%
g134 14
 
0.3%
g414 9
 
0.2%
g118 3
 
0.1%
g58 2
 
< 0.1%
Other values (18) 18
 
0.3%

Most occurring characters

ValueCountFrequency (%)
G 5347
25.2%
1 5257
24.7%
3 5155
24.3%
6 5090
24.0%
9 126
 
0.6%
2 123
 
0.6%
0 77
 
0.4%
4 55
 
0.3%
8 12
 
0.1%
5 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 5347
25.2%
1 5257
24.7%
3 5155
24.3%
6 5090
24.0%
9 126
 
0.6%
2 123
 
0.6%
0 77
 
0.4%
4 55
 
0.3%
8 12
 
0.1%
5 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 5347
25.2%
1 5257
24.7%
3 5155
24.3%
6 5090
24.0%
9 126
 
0.6%
2 123
 
0.6%
0 77
 
0.4%
4 55
 
0.3%
8 12
 
0.1%
5 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 5347
25.2%
1 5257
24.7%
3 5155
24.3%
6 5090
24.0%
9 126
 
0.6%
2 123
 
0.6%
0 77
 
0.4%
4 55
 
0.3%
8 12
 
0.1%
5 7
 
< 0.1%

tipo_mat2
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
H3
5070 
H1
 
237
H2
 
40

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10694
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH1
2nd rowH1
3rd rowH1
4th rowH1
5th rowH1

Common Values

ValueCountFrequency (%)
H3 5070
94.8%
H1 237
 
4.4%
H2 40
 
0.7%

Length

2024-12-10T22:06:55.219331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.261052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
h3 5070
94.8%
h1 237
 
4.4%
h2 40
 
0.7%

Most occurring characters

ValueCountFrequency (%)
H 5347
50.0%
3 5070
47.4%
1 237
 
2.2%
2 40
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 5347
50.0%
3 5070
47.4%
1 237
 
2.2%
2 40
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 5347
50.0%
3 5070
47.4%
1 237
 
2.2%
2 40
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 5347
50.0%
3 5070
47.4%
1 237
 
2.2%
2 40
 
0.4%

clase_sop22
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing277
Missing (%)5.2%
Memory size41.9 KiB
J3
5070 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJ3
2nd rowJ3
3rd rowJ3
4th rowJ3
5th rowJ3

Common Values

ValueCountFrequency (%)
J3 5070
94.8%
(Missing) 277
 
5.2%

Length

2024-12-10T22:06:55.305018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.341379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
j3 5070
100.0%

Most occurring characters

ValueCountFrequency (%)
J 5070
50.0%
3 5070
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 5070
50.0%
3 5070
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 5070
50.0%
3 5070
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 5070
50.0%
3 5070
50.0%

clas_sop32
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing277
Missing (%)5.2%
Memory size41.9 KiB
K1
5070 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10140
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK1
2nd rowK1
3rd rowK1
4th rowK1
5th rowK1

Common Values

ValueCountFrequency (%)
K1 5070
94.8%
(Missing) 277
 
5.2%

Length

2024-12-10T22:06:55.380442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.418937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
k1 5070
100.0%

Most occurring characters

ValueCountFrequency (%)
K 5070
50.0%
1 5070
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 5070
50.0%
1 5070
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 5070
50.0%
1 5070
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 5070
50.0%
1 5070
50.0%

cyber_monday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)0.1%
Memory size41.9 KiB
0.0
5322 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16029
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5322
99.5%
1.0 21
 
0.4%
(Missing) 4
 
0.1%

Length

2024-12-10T22:06:55.459651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.496302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5322
99.6%
1.0 21
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

black_friday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)0.1%
Memory size41.9 KiB
0.0
5322 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16029
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5322
99.5%
1.0 21
 
0.4%
(Missing) 4
 
0.1%

Length

2024-12-10T22:06:55.536893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.573798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5322
99.6%
1.0 21
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10665
66.5%
. 5343
33.3%
1 21
 
0.1%

es_festivo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)0.1%
Memory size41.9 KiB
0.0
5029 
1.0
 
314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16029
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5029
94.1%
1.0 314
 
5.9%
(Missing) 4
 
0.1%

Length

2024-12-10T22:06:55.612983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T22:06:55.649294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5029
94.1%
1.0 314
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 10372
64.7%
. 5343
33.3%
1 314
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10372
64.7%
. 5343
33.3%
1 314
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10372
64.7%
. 5343
33.3%
1 314
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10372
64.7%
. 5343
33.3%
1 314
 
2.0%

ee_comercio
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11143.967
Minimum7445.216
Maximum12359.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:55.688957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7445.216
5-th percentile9968.6212
Q110698.386
median11298.868
Q311553.241
95-th percentile12359.999
Maximum12359.999
Range4914.7828
Interquartile range (IQR)854.85524

Descriptive statistics

Standard deviation713.82957
Coefficient of variation (CV)0.064055246
Kurtosis-0.70328304
Mean11143.967
Median Absolute Deviation (MAD)519.67549
Skewness-0.27278213
Sum59542218
Variance509552.65
MonotonicityNot monotonic
2024-12-10T22:06:55.739460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
11169.79145 345
 
6.5%
11524.09431 339
 
6.3%
10743.04633 315
 
5.9%
11818.54374 313
 
5.9%
12175.83687 296
 
5.5%
10162.2901 288
 
5.4%
11701.73392 282
 
5.3%
10920.24966 280
 
5.2%
12359.99874 278
 
5.2%
11298.86825 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
7445.215981 1
 
< 0.1%
9340.23361 6
 
0.1%
9811.477166 259
4.8%
9968.621227 117
2.2%
10023.7495 4
 
0.1%
10070.52798 273
5.1%
10153.75237 26
 
0.5%
10162.2901 288
5.4%
10189.8859 2
 
< 0.1%
10390.99176 162
3.0%
ValueCountFrequency (%)
12359.99874 278
5.2%
12175.83687 296
5.5%
12092.61387 125
 
2.3%
11818.54374 313
5.9%
11701.73392 282
5.3%
11688.7245 9
 
0.2%
11679.97085 29
 
0.5%
11553.24134 136
2.5%
11524.09431 339
6.3%
11519.62721 113
 
2.1%

icc
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean30.772438
Minimum24.3427
Maximum38.7632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:55.877282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum24.3427
5-th percentile26.0169
Q128.9779
median30.3134
Q333.0606
95-th percentile34.258
Maximum38.7632
Range14.4205
Interquartile range (IQR)4.0827

Descriptive statistics

Standard deviation2.5355223
Coefficient of variation (CV)0.082395883
Kurtosis-0.78820931
Mean30.772438
Median Absolute Deviation (MAD)1.912
Skewness-0.18295197
Sum164417.14
Variance6.4288731
MonotonicityNot monotonic
2024-12-10T22:06:55.929986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
28.9779 345
 
6.5%
32.7338 339
 
6.3%
28.4014 315
 
5.9%
33.0606 313
 
5.9%
31.2603 296
 
5.5%
30.3134 288
 
5.4%
33.8365 282
 
5.3%
34.1798 280
 
5.2%
29.7094 278
 
5.2%
34.258 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
24.3427 6
 
0.1%
25.5818 136
2.5%
25.8171 117
 
2.2%
26.0169 113
 
2.1%
26.3704 99
 
1.9%
27.7105 77
 
1.4%
28.1936 125
 
2.3%
28.4014 315
5.9%
28.6796 105
 
2.0%
28.7609 9
 
0.2%
ValueCountFrequency (%)
38.7632 2
 
< 0.1%
36.8885 4
 
0.1%
36.3435 26
 
0.5%
34.9611 9
 
0.2%
34.599 6
 
0.1%
34.258 275
5.1%
34.1798 280
5.2%
33.887 162
3.0%
33.8365 282
5.3%
33.1164 272
5.1%

imacec_comercio
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean110.94681
Minimum98.979239
Maximum144.90812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:55.982731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum98.979239
5-th percentile99.322139
Q1104.12787
median109.20138
Q3116.99508
95-th percentile127.09627
Maximum144.90812
Range45.928876
Interquartile range (IQR)12.867204

Descriptive statistics

Standard deviation9.2037813
Coefficient of variation (CV)0.082956702
Kurtosis-0.49984159
Mean110.94681
Median Absolute Deviation (MAD)5.7826587
Skewness0.64978071
Sum592788.8
Variance84.70959
MonotonicityNot monotonic
2024-12-10T22:06:56.033649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
105.1948858 345
 
6.5%
116.9950758 339
 
6.3%
110.4442759 315
 
5.9%
113.3612279 313
 
5.9%
123.0552463 296
 
5.5%
100.6797841 288
 
5.4%
104.1278722 282
 
5.3%
114.4787998 280
 
5.2%
127.0962716 278
 
5.2%
125.2755383 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
98.97923873 259
4.8%
99.32213905 257
4.8%
100.6797841 288
5.4%
101.7346627 272
5.1%
103.4187246 162
3.0%
104.1278722 282
5.3%
104.2572439 6
 
0.1%
104.8332553 5
 
0.1%
105.1948858 345
6.5%
105.4043919 113
 
2.1%
ValueCountFrequency (%)
144.908115 9
 
0.2%
132.5659443 125
2.3%
127.0962716 278
5.2%
125.2755383 275
5.1%
124.8978426 29
 
0.5%
124.5061624 4
 
0.1%
123.0552463 296
5.5%
119.9448786 19
 
0.4%
119.6414778 105
 
2.0%
117.2338598 26
 
0.5%

imacec_general
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean108.7317
Minimum93.924302
Maximum120.21249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.083462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum93.924302
5-th percentile102.35593
Q1105.03835
median107.73843
Q3112.05388
95-th percentile117.38566
Maximum120.21249
Range26.288189
Interquartile range (IQR)7.0155284

Descriptive statistics

Standard deviation4.4586516
Coefficient of variation (CV)0.041005995
Kurtosis-0.49246095
Mean108.7317
Median Absolute Deviation (MAD)2.8257306
Skewness0.42521153
Sum580953.48
Variance19.879574
MonotonicityNot monotonic
2024-12-10T22:06:56.136146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
107.0402978 345
 
6.5%
114.863457 339
 
6.3%
108.0013717 315
 
5.9%
111.2184138 313
 
5.9%
114.1172433 296
 
5.5%
102.3559334 288
 
5.4%
112.0538802 282
 
5.3%
104.2573099 280
 
5.2%
117.3856586 278
 
5.2%
107.6344397 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
93.92430184 1
 
< 0.1%
99.05236949 6
 
0.1%
99.67387234 117
2.2%
100.1395693 6
 
0.1%
102.3559334 288
5.4%
103.6627204 4
 
0.1%
104.2573099 280
5.2%
104.6103544 2
 
< 0.1%
104.6971191 113
 
2.1%
104.9126947 162
3.0%
ValueCountFrequency (%)
120.212491 9
 
0.2%
118.228994 125
 
2.3%
117.3856586 278
5.2%
115.1952382 29
 
0.5%
114.8887802 19
 
0.4%
114.863457 339
6.3%
114.1172433 296
5.5%
112.0538802 282
5.3%
111.2184138 313
5.9%
110.8928683 136
2.5%

imacec_no_minero
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean112.16644
Minimum95.27388
Maximum123.28591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.183462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum95.27388
5-th percentile104.66235
Q1107.86973
median111.17488
Q3115.88451
95-th percentile121.11006
Maximum123.28591
Range28.012034
Interquartile range (IQR)8.0147814

Descriptive statistics

Standard deviation4.9882263
Coefficient of variation (CV)0.044471648
Kurtosis-0.90480966
Mean112.16644
Median Absolute Deviation (MAD)3.733904
Skewness0.31660179
Sum599305.27
Variance24.882402
MonotonicityNot monotonic
2024-12-10T22:06:56.232337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
110.1493188 345
 
6.5%
118.8297168 339
 
6.3%
112.6344182 315
 
5.9%
115.884513 313
 
5.9%
119.519957 296
 
5.5%
104.6623547 288
 
5.4%
115.5507249 282
 
5.3%
107.4409766 280
 
5.2%
121.1100633 278
 
5.2%
111.1194342 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
95.27388001 1
 
< 0.1%
102.4920407 6
 
0.1%
102.9378149 6
 
0.1%
103.6425749 117
2.2%
104.6623547 288
5.4%
106.4917733 257
4.8%
106.8519779 2
 
< 0.1%
106.8536327 162
3.0%
107.2515335 4
 
0.1%
107.4409766 280
5.2%
ValueCountFrequency (%)
123.2859141 9
 
0.2%
121.2453744 125
 
2.3%
121.1100633 278
5.2%
119.519957 296
5.5%
119.4412315 29
 
0.5%
118.8297168 339
6.3%
117.8210718 19
 
0.4%
115.884513 313
5.9%
115.5507249 282
5.3%
114.3432508 136
2.5%

imce_comercio
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean46.288166
Minimum39.273927
Maximum61.214953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.282386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum39.273927
5-th percentile40.291262
Q143.956044
median46.415771
Q348.387097
95-th percentile52.55102
Maximum61.214953
Range21.941026
Interquartile range (IQR)4.4310528

Descriptive statistics

Standard deviation3.3530199
Coefficient of variation (CV)0.07243795
Kurtosis0.21883305
Mean46.288166
Median Absolute Deviation (MAD)1.9713262
Skewness0.32206184
Sum247317.67
Variance11.242742
MonotonicityNot monotonic
2024-12-10T22:06:56.331115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
46.55797101 345
 
6.5%
52.55102041 339
 
6.3%
43.95604396 315
 
5.9%
48.38709677 313
 
5.9%
44.83333333 296
 
5.5%
46.41577061 288
 
5.4%
44.76744186 282
 
5.3%
50 280
 
5.2%
46.48148148 278
 
5.2%
48.99598394 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
39.27392739 77
 
1.4%
39.9691358 99
 
1.9%
40.29126214 136
2.5%
40.86538462 6
 
0.1%
40.88050314 113
 
2.1%
42.32026144 125
 
2.3%
42.85714286 117
 
2.2%
42.94871795 105
 
2.0%
43.05555556 273
5.1%
43.95604396 315
5.9%
ValueCountFrequency (%)
61.21495327 2
 
< 0.1%
58.4045584 1
 
< 0.1%
57.97101449 26
 
0.5%
55.72916667 4
 
0.1%
53.92156863 19
 
0.4%
52.55102041 339
6.3%
51.85185185 6
 
0.1%
51.48514851 9
 
0.2%
51.48148148 162
3.0%
51.10062893 29
 
0.5%

imce_general
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean42.383026
Minimum35.611146
Maximum60.679626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.379166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum35.611146
5-th percentile35.678785
Q141.048919
median43.193503
Q343.914509
95-th percentile46.309646
Maximum60.679626
Range25.06848
Interquartile range (IQR)2.86559

Descriptive statistics

Standard deviation2.9472462
Coefficient of variation (CV)0.069538361
Kurtosis4.4935631
Mean42.383026
Median Absolute Deviation (MAD)1.791508
Skewness0.260961
Sum226452.51
Variance8.6862601
MonotonicityNot monotonic
2024-12-10T22:06:56.431863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
43.19350295 345
 
6.5%
46.30964599 339
 
6.3%
41.35517932 315
 
5.9%
44.07191795 313
 
5.9%
40.54936576 296
 
5.5%
41.05146701 288
 
5.4%
42.66088781 282
 
5.3%
44.86759784 280
 
5.2%
35.67878501 278
 
5.2%
43.71039206 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
35.61114643 125
2.3%
35.67878501 278
5.2%
38.95840316 77
 
1.4%
39.01191138 6
 
0.1%
39.49184425 259
4.8%
40.4926985 99
 
1.9%
40.54936576 296
5.5%
40.67137089 105
 
2.0%
41.04891901 136
2.5%
41.05146701 288
5.4%
ValueCountFrequency (%)
60.67962622 2
 
< 0.1%
58.2032133 26
0.5%
55.721718 1
 
< 0.1%
53.34668223 4
 
0.1%
51.19457424 6
 
0.1%
49.0422153 19
0.4%
47.49531277 29
0.5%
47.47006128 4
 
0.1%
46.56308462 9
 
0.2%
46.47943242 9
 
0.2%

ine_alimentos
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean107.77205
Minimum99.780515
Maximum142.8552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.482016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum99.780515
5-th percentile100.66549
Q1102.32546
median105.28513
Q3110.65052
95-th percentile125.74814
Maximum142.8552
Range43.074684
Interquartile range (IQR)8.3250652

Descriptive statistics

Standard deviation7.0132936
Coefficient of variation (CV)0.065075257
Kurtosis1.8688715
Mean107.77205
Median Absolute Deviation (MAD)3.2198318
Skewness1.4659612
Sum575826.04
Variance49.186287
MonotonicityNot monotonic
2024-12-10T22:06:56.530682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
104.7502145 345
 
6.5%
114.670991 339
 
6.3%
102.9569663 315
 
5.9%
102.0652934 313
 
5.9%
110.6505245 296
 
5.5%
108.3832401 288
 
5.4%
102.0065957 282
 
5.3%
101.5281426 280
 
5.2%
125.7481355 278
 
5.2%
100.6654889 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
99.78051535 117
 
2.2%
100.6654889 275
5.1%
101.2685205 1
 
< 0.1%
101.5281426 280
5.2%
102.0065957 282
5.3%
102.0652934 313
5.9%
102.3254593 105
 
2.0%
102.9569663 315
5.9%
103.4790249 136
2.5%
103.8085638 259
4.8%
ValueCountFrequency (%)
142.8551996 9
 
0.2%
126.2715334 26
 
0.5%
125.7481355 278
5.2%
125.4051132 125
 
2.3%
123.0426065 2
 
< 0.1%
120.3059932 19
 
0.4%
119.9015095 29
 
0.5%
116.650294 4
 
0.1%
114.8551135 4
 
0.1%
114.670991 339
6.3%

ine_supermercados
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean103.31423
Minimum95.289245
Maximum148.8181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.584357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum95.289245
5-th percentile95.289245
Q197.681779
median101.22285
Q3105.15462
95-th percentile125.97844
Maximum148.8181
Range53.528855
Interquartile range (IQR)7.4728364

Descriptive statistics

Standard deviation8.701201
Coefficient of variation (CV)0.084220742
Kurtosis2.6739156
Mean103.31423
Median Absolute Deviation (MAD)3.5410689
Skewness1.7114427
Sum552007.92
Variance75.710899
MonotonicityNot monotonic
2024-12-10T22:06:56.642803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
97.705323 345
 
6.5%
112.0636656 339
 
6.3%
95.31695953 315
 
5.9%
95.28924509 313
 
5.9%
106.2890514 296
 
5.5%
101.9854126 288
 
5.4%
95.70516742 282
 
5.3%
100.3724447 280
 
5.2%
125.9784373 278
 
5.2%
96.16343204 275
 
5.1%
Other values (25) 2332
43.6%
ValueCountFrequency (%)
95.28924509 313
5.9%
95.31695953 315
5.9%
95.70516742 282
5.3%
96.16343204 275
5.1%
97.681779 217
4.1%
97.705323 345
6.5%
99.09567248 105
 
2.0%
99.13270904 259
4.8%
99.33852859 117
 
2.2%
99.41228622 136
 
2.5%
ValueCountFrequency (%)
148.8181 9
 
0.2%
127.4831412 125
2.3%
126.5410863 26
 
0.5%
125.9784373 278
5.2%
120.7632509 2
 
< 0.1%
120.6900743 19
 
0.4%
118.6654999 29
 
0.5%
116.6407562 4
 
0.1%
113.9610434 6
 
0.1%
113.010931 4
 
0.1%

tpm
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.4%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean8.9375819
Minimum0.5
Maximum11.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.696168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile6
Q16.5
median8.75
Q311.25
95-th percentile11.25
Maximum11.25
Range10.75
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.1135376
Coefficient of variation (CV)0.23647756
Kurtosis-0.94120582
Mean8.9375819
Median Absolute Deviation (MAD)2.5
Skewness-0.39129089
Sum47753.5
Variance4.4670411
MonotonicityNot monotonic
2024-12-10T22:06:56.746810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11.25 1703
31.8%
6 530
 
9.9%
8.75 529
 
9.9%
10.5 387
 
7.2%
9.25 345
 
6.5%
6.5 339
 
6.3%
8.25 282
 
5.3%
6.25 280
 
5.2%
8.5 278
 
5.2%
7.5 275
 
5.1%
Other values (13) 395
 
7.4%
ValueCountFrequency (%)
0.5 1
 
< 0.1%
2 2
 
< 0.1%
2.25 26
 
0.5%
3.75 19
 
0.4%
4 9
 
0.2%
5 4
 
0.1%
5.75 162
 
3.0%
6 530
9.9%
6.25 280
5.2%
6.5 339
6.3%
ValueCountFrequency (%)
11.25 1703
31.8%
11 113
 
2.1%
10.5 387
 
7.2%
9.5 11
 
0.2%
9.25 345
 
6.5%
9 9
 
0.2%
8.75 529
 
9.9%
8.5 278
 
5.2%
8.25 282
 
5.3%
8 4
 
0.1%

pib
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.2%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17445.111
Minimum16514.041
Maximum18977.031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.795907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum16514.041
5-th percentile16561.769
Q116569.308
median18014.821
Q318132.62
95-th percentile18663.912
Maximum18977.031
Range2462.9906
Interquartile range (IQR)1563.3118

Descriptive statistics

Standard deviation893.93106
Coefficient of variation (CV)0.051242497
Kurtosis-1.8164013
Mean17445.111
Median Absolute Deviation (MAD)962.21037
Skewness0.12536146
Sum93209229
Variance799112.74
MonotonicityNot monotonic
2024-12-10T22:06:56.843782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
18132.61978 894
16.7%
16569.30801 890
16.6%
16584.23427 847
15.8%
18530.78056 832
15.6%
16561.76868 692
12.9%
18014.82071 518
9.7%
18663.91151 338
 
6.3%
16527.7374 218
 
4.1%
18977.03108 54
 
1.0%
18184.99557 39
 
0.7%
Other values (3) 21
 
0.4%
ValueCountFrequency (%)
16514.04051 2
 
< 0.1%
16527.7374 218
 
4.1%
16561.76868 692
12.9%
16569.30801 890
16.6%
16584.23427 847
15.8%
16637.83932 18
 
0.3%
17172.91795 1
 
< 0.1%
18014.82071 518
9.7%
18132.61978 894
16.7%
18184.99557 39
 
0.7%
ValueCountFrequency (%)
18977.03108 54
 
1.0%
18663.91151 338
 
6.3%
18530.78056 832
15.6%
18184.99557 39
 
0.7%
18132.61978 894
16.7%
18014.82071 518
9.7%
17172.91795 1
 
< 0.1%
16637.83932 18
 
0.3%
16584.23427 847
15.8%
16569.30801 890
16.6%

tavg
Real number (ℝ)

High correlation 

Distinct170
Distinct (%)3.2%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean15.48731
Minimum5.9
Maximum26.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:56.901924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile9.8
Q112.2
median13.9
Q319.1
95-th percentile23.7
Maximum26.9
Range21
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation4.5977018
Coefficient of variation (CV)0.29686896
Kurtosis-0.48001898
Mean15.48731
Median Absolute Deviation (MAD)2.7
Skewness0.64341114
Sum82748.7
Variance21.138862
MonotonicityNot monotonic
2024-12-10T22:06:56.961196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.7 342
 
6.4%
12.4 314
 
5.9%
16.4 209
 
3.9%
12.9 197
 
3.7%
12.2 192
 
3.6%
22.2 191
 
3.6%
9.8 184
 
3.4%
16.5 174
 
3.3%
11.2 174
 
3.3%
11 173
 
3.2%
Other values (160) 3193
59.7%
ValueCountFrequency (%)
5.9 4
 
0.1%
6.1 1
 
< 0.1%
6.3 1
 
< 0.1%
6.7 1
 
< 0.1%
6.8 2
 
< 0.1%
6.9 14
0.3%
7.1 2
 
< 0.1%
7.3 7
0.1%
7.4 4
 
0.1%
7.5 2
 
< 0.1%
ValueCountFrequency (%)
26.9 5
 
0.1%
26.7 162
3.0%
25.6 7
 
0.1%
25.2 10
 
0.2%
25 7
 
0.1%
24.9 9
 
0.2%
24.8 14
 
0.3%
24.7 4
 
0.1%
24.6 7
 
0.1%
24.5 15
 
0.3%

tmin
Real number (ℝ)

High correlation 

Distinct160
Distinct (%)3.0%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.222197
Minimum-1
Maximum18.8
Zeros1
Zeros (%)< 0.1%
Negative7
Negative (%)0.1%
Memory size41.9 KiB
2024-12-10T22:06:57.087978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4.2
Q16.8
median10.3
Q313.9
95-th percentile16.2
Maximum18.8
Range19.8
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation4.0021505
Coefficient of variation (CV)0.39151568
Kurtosis-0.90054371
Mean10.222197
Median Absolute Deviation (MAD)3.5
Skewness-0.024567319
Sum54617.2
Variance16.017208
MonotonicityNot monotonic
2024-12-10T22:06:57.145317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 376
 
7.0%
13.9 197
 
3.7%
4.2 191
 
3.6%
5.8 187
 
3.5%
11.1 185
 
3.5%
6.7 176
 
3.3%
8.5 175
 
3.3%
11.2 174
 
3.3%
9.4 173
 
3.2%
8.2 169
 
3.2%
Other values (150) 3340
62.5%
ValueCountFrequency (%)
-1 4
 
0.1%
-0.6 3
 
0.1%
0 1
 
< 0.1%
0.5 3
 
0.1%
0.9 6
 
0.1%
1 17
0.3%
1.1 5
 
0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.6 1
 
< 0.1%
ValueCountFrequency (%)
18.8 9
 
0.2%
18.1 162
3.0%
17.8 2
 
< 0.1%
17.6 1
 
< 0.1%
17.4 3
 
0.1%
17.3 5
 
0.1%
17.1 7
 
0.1%
17 12
 
0.2%
16.9 7
 
0.1%
16.8 4
 
0.1%

tmax
Real number (ℝ)

High correlation 

Distinct214
Distinct (%)4.0%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean23.180928
Minimum10
Maximum36.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:57.202131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.4
Q116.9
median22.7
Q329.4
95-th percentile33.2
Maximum36.6
Range26.6
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation6.6007766
Coefficient of variation (CV)0.28475031
Kurtosis-1.1997574
Mean23.180928
Median Absolute Deviation (MAD)6.2
Skewness0.16139238
Sum123855.7
Variance43.570252
MonotonicityNot monotonic
2024-12-10T22:06:57.258687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.4 323
 
6.0%
29.7 211
 
3.9%
15.9 191
 
3.6%
19.1 185
 
3.5%
24.7 174
 
3.3%
15.1 171
 
3.2%
16.6 171
 
3.2%
18.8 166
 
3.1%
21.8 165
 
3.1%
14.4 164
 
3.1%
Other values (204) 3422
64.0%
ValueCountFrequency (%)
10 1
 
< 0.1%
10.8 17
0.3%
11.1 6
 
0.1%
11.2 4
 
0.1%
11.5 2
 
< 0.1%
11.6 1
 
< 0.1%
12.2 27
0.5%
12.4 18
0.3%
12.5 1
 
< 0.1%
12.6 2
 
< 0.1%
ValueCountFrequency (%)
36.6 5
 
0.1%
36 161
3.0%
35.8 7
 
0.1%
35.6 3
 
0.1%
35.3 1
 
< 0.1%
34.9 5
 
0.1%
34.6 4
 
0.1%
34.3 7
 
0.1%
34.2 10
 
0.2%
34 6
 
0.1%

wspd
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)1.3%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.9802545
Minimum0.2
Maximum7.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.9 KiB
2024-12-10T22:06:57.313111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q11.7
median2.8
Q34.2
95-th percentile5.7
Maximum7.1
Range6.9
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.5012603
Coefficient of variation (CV)0.50373561
Kurtosis-0.90987324
Mean2.9802545
Median Absolute Deviation (MAD)1.3
Skewness0.25311782
Sum15923.5
Variance2.2537826
MonotonicityNot monotonic
2024-12-10T22:06:57.371765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 417
 
7.8%
2.8 284
 
5.3%
1.8 245
 
4.6%
1.5 230
 
4.3%
4 223
 
4.2%
4.3 223
 
4.2%
2.5 220
 
4.1%
0.6 211
 
3.9%
3.7 206
 
3.9%
1.3 206
 
3.9%
Other values (57) 2878
53.8%
ValueCountFrequency (%)
0.2 9
 
0.2%
0.3 7
 
0.1%
0.4 16
 
0.3%
0.5 10
 
0.2%
0.6 211
3.9%
0.7 28
 
0.5%
0.8 54
 
1.0%
0.9 47
 
0.9%
1 17
 
0.3%
1.1 201
3.8%
ValueCountFrequency (%)
7.1 9
 
0.2%
6.9 2
 
< 0.1%
6.8 2
 
< 0.1%
6.7 4
 
0.1%
6.5 17
0.3%
6.3 5
 
0.1%
6.2 8
 
0.1%
6.1 2
 
< 0.1%
6 14
0.3%
5.9 30
0.6%

Interactions

2024-12-10T22:06:49.897845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.051223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.934757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.726449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.606028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.600296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.831753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.886608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.793945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.803832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.646344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.647982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.573297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-12-10T22:06:37.065078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.380742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.438290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.267868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.311908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.068448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.170139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.330827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.377276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.150500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.072611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.833148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.803809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.558456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.430779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.649222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.452793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.189406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.333253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.127180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.564207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.481481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.315011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.349902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.104342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.213654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.370137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.417833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.186578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.109834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.869618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.843875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.595987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.470266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.693043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.492204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.227887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.372498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.516968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.613740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.523083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.356601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.390405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.144672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.254066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.413545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.459686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.224264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.149231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.909963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.884633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.634658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.507751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.732632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.530048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.265715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.412006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.587532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.656106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.568210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.513696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.429359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.185120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.317413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.453860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.500618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.261028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.186025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.982961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.923380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.671024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.547072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.772879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.569021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.303488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.449597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.645670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.703080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.615660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.600875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.469427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.226181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.385801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.520789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.541879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.297821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.224170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.035782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.961975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.725258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.583525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.810847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.606149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.370225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.484488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.688532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.745961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.655563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.665724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.509359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.276880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.430041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.563440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.582677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.339763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.260832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.075192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.999587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.776744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.624714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.853646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.647744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.471601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.524441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.740481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.794028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.705205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.721562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.555113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.395712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.480793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.606013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.631442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.411533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.307280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.123698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.044158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.820700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:50.662856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:33.892850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:34.685657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:35.546802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:36.560938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:37.784794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:38.836170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:39.747894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:40.762682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:41.599111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:42.581167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:43.530290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:44.644659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:45.673626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:46.464875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:47.349624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:48.173182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.083163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-10T22:06:49.857574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-10T22:06:57.436259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
black_fridaycanal2canal_sap2cantidadcategoria2categoria_22centro_costo_cuenta_clave2clase_sop2clasificacion2codigo_activo2codigo_producto2costo_totalcuenta_clave2cyber_mondaydescripcion_producto2descripcion_tipo_factura2ee_comercioes_festivoestimadogrado2iccid_tipo_factura2imacec_comercioimacec_generalimacec_no_mineroimce_comercioimce_generaline_alimentosine_supermercadoslinea2marca2pibstock_disponible_totaltavgtipo_mat2tmaxtmintpmventa_total_netowspd
black_friday1.0000.0000.0350.0460.0150.0380.0000.0160.0130.0000.0000.0250.0000.0000.0000.0620.0830.0000.0130.0380.1270.0420.1020.1440.1080.0750.0970.0700.0540.0180.0150.1120.0190.1330.0230.1310.0770.0680.0000.136
canal20.0001.0001.0000.1820.5010.5680.8500.5060.6020.5940.6110.2140.8500.0000.5970.3390.0730.0040.6020.5680.0920.3450.0590.0790.0860.0950.1240.1010.1090.4730.5010.1210.1420.0410.4710.0570.0360.1260.2920.033
canal_sap20.0351.0001.0000.4100.6030.5030.7540.6080.9290.5350.5460.3420.7540.0140.5370.3010.1160.0360.9290.5030.2390.4100.1390.1210.0800.2220.2920.2530.2600.7110.6030.3150.3410.0600.7200.0590.0410.2900.4430.063
cantidad0.0460.1820.4101.0000.4880.4070.3620.4910.7640.4340.4380.9910.3620.0340.4350.4150.0500.2440.7640.407-0.1860.3260.1170.0320.031-0.154-0.0550.0780.1100.5710.4880.0900.2310.1110.5770.0900.0380.1160.9960.158
categoria20.0150.5010.6030.4881.0000.9880.6011.0001.0000.9980.9980.5380.6010.0000.9980.3280.1280.0381.0000.9880.2870.4450.1630.1370.1020.2730.3290.3270.3341.0001.0000.3120.4110.0870.8390.0850.0730.3250.5590.089
categoria_220.0380.5680.5030.4070.9881.0000.5090.9881.0000.9980.9980.5270.5090.0000.9980.4190.1050.0331.0001.0000.2250.4790.1300.1100.0850.2170.2580.2550.2630.9960.9880.2750.3330.0830.9430.0770.0730.2570.5260.079
centro_costo_cuenta_clave20.0000.8500.7540.3620.6010.5091.0000.5990.9110.3580.3650.2611.0000.0000.3590.2110.0740.0660.9110.5090.1520.2530.0920.0790.0700.1380.1720.1900.1960.6910.6010.2460.2240.0500.6810.0500.0210.1830.3600.039
clase_sop20.0160.5060.6080.4911.0000.9880.5991.0001.0000.9980.9980.5450.5990.0000.9980.3330.1290.0371.0000.9880.2880.4450.1630.1380.1040.2740.3300.3280.3351.0001.0000.3140.4130.0880.8470.0870.0730.3250.5580.090
clasificacion20.0130.6020.9290.7641.0001.0000.9111.0001.0000.9980.9970.6520.9110.0000.9970.4200.1720.0400.9981.0000.4120.6030.2180.1850.1470.3810.4740.4520.4641.0001.0000.4410.5710.1261.0000.1080.0820.4560.7870.108
codigo_activo20.0000.5940.5350.4340.9980.9980.3580.9980.9981.0001.0000.6020.3580.0001.0000.3490.1030.0000.9980.9980.2200.5210.1170.0990.0780.2630.2890.2620.2700.9980.9980.3390.4840.0570.9980.0500.0460.2530.4900.055
codigo_producto20.0000.6110.5460.4380.9980.9980.3650.9980.9971.0001.0000.6030.3650.0001.0000.3490.1040.0000.9970.9980.2240.5230.1190.1000.0790.2650.2940.2690.2760.9970.9980.3470.4880.0570.9970.0510.0470.2590.4930.056
costo_total0.0250.2140.3420.9910.5380.5270.2610.5450.6520.6020.6031.0000.2610.0220.6020.3620.0540.0330.6520.527-0.1810.3650.1310.0390.042-0.147-0.0480.0760.1100.6260.5380.0920.2280.1210.5490.0970.0470.1100.9870.158
cuenta_clave20.0000.8500.7540.3620.6010.5091.0000.5990.9110.3580.3650.2611.0000.0000.3590.2110.0740.0660.9110.5090.1520.2530.0920.0790.0700.1380.1720.1900.1960.6910.6010.2460.2240.0500.6810.0500.0210.1830.3600.039
cyber_monday0.0000.0000.0140.0340.0000.0000.0000.0000.0000.0000.0000.0220.0001.0000.0000.0440.0410.0000.0000.0000.1180.0450.0440.0440.0530.1300.1020.0910.1050.0080.0000.1170.1590.0550.0000.0710.0770.0820.0370.043
descripcion_producto20.0000.5970.5370.4350.9980.9980.3590.9980.9971.0001.0000.6020.3590.0001.0000.3490.1030.0000.9970.9980.2200.5210.1170.0980.0780.2630.2900.2630.2710.9980.9980.3400.4840.0570.9980.0490.0460.2540.4910.054
descripcion_tipo_factura20.0620.3390.3010.4150.3280.4190.2110.3330.4200.3490.3490.3620.2110.0440.3491.0000.1060.2960.4200.4190.1510.9480.1390.1090.0930.2160.1020.2220.2130.3950.3280.0830.1540.1550.3280.1210.1090.1080.3160.112
ee_comercio0.0830.0730.1160.0500.1280.1050.0740.1290.1720.1030.1040.0540.0740.0410.1030.1061.0000.3280.1720.1050.1330.0450.5650.8370.7980.095-0.0090.2610.1650.1450.1280.355-0.0220.4150.1400.2780.364-0.2560.0540.481
es_festivo0.0000.0040.0360.2440.0380.0330.0660.0370.0400.0000.0000.0330.0660.0000.0000.2960.3281.0000.0400.0330.3870.0430.3360.3450.4470.4100.3940.3110.2500.0400.0380.1980.0000.5300.0400.6630.4170.2230.0450.381
estimado0.0130.6020.9290.7641.0001.0000.9111.0000.9980.9980.9970.6520.9110.0000.9970.4200.1720.0401.0001.0000.4120.6030.2180.1850.1470.3810.4740.4520.4641.0001.0000.4410.5710.1261.0000.1080.0820.4560.7870.108
grado20.0380.5680.5030.4070.9881.0000.5090.9881.0000.9980.9980.5270.5090.0000.9980.4190.1050.0331.0001.0000.2250.4790.1300.1100.0850.2170.2580.2550.2630.9960.9880.2750.3330.0830.9430.0770.0730.2570.5260.079
icc0.1270.0920.239-0.1860.2870.2250.1520.2880.4120.2200.224-0.1810.1520.1180.2200.1510.1330.3870.4120.2251.0000.1970.0670.062-0.0050.7020.537-0.255-0.1330.3350.2870.1890.1040.2780.3060.1720.312-0.680-0.1690.153
id_tipo_factura20.0420.3450.4100.3260.4450.4790.2530.4450.6030.5210.5230.3650.2530.0450.5210.9480.0450.0430.6030.4790.1971.0000.1330.0450.0520.1960.2370.2480.2550.5300.4450.3820.2920.0520.4620.0360.0380.2780.3830.056
imacec_comercio0.1020.0590.1390.1170.1630.1300.0920.1630.2180.1170.1190.1310.0920.0440.1170.1390.5650.3360.2180.1300.0670.1331.0000.5510.6380.2080.0230.1050.2190.1880.1630.3720.1500.6860.1670.6670.652-0.1600.1180.618
imacec_general0.1440.0790.1210.0320.1370.1100.0790.1380.1850.0990.1000.0390.0790.0440.0980.1090.8370.3450.1850.1100.0620.0450.5511.0000.9590.087-0.0240.3390.1730.1580.1370.4440.1150.3650.1420.2230.291-0.1630.0360.395
imacec_no_minero0.1080.0860.0800.0310.1020.0850.0700.1040.1470.0780.0790.0420.0700.0530.0780.0930.7980.4470.1470.085-0.0050.0520.6380.9591.0000.006-0.1030.2980.1570.1200.1020.3810.0770.3940.1040.3130.339-0.0660.0330.370
imce_comercio0.0750.0950.222-0.1540.2730.2170.1380.2740.3810.2630.265-0.1470.1380.1300.2630.2160.0950.4100.3810.2170.7020.1960.2080.0870.0061.0000.745-0.022-0.0280.3140.273-0.0940.2020.2550.2870.0760.226-0.853-0.140-0.014
imce_general0.0970.1240.292-0.0550.3290.2580.1720.3300.4740.2890.294-0.0480.1720.1020.2900.102-0.0090.3940.4740.2580.5370.2370.023-0.024-0.1030.7451.000-0.220-0.2190.3840.329-0.1510.2280.1650.3550.0100.154-0.773-0.044-0.016
ine_alimentos0.0700.1010.2530.0780.3270.2550.1900.3280.4520.2620.2690.0760.1900.0910.2630.2220.2610.3110.4520.255-0.2550.2480.1050.3390.298-0.022-0.2201.0000.8740.3820.3270.0580.185-0.0960.351-0.067-0.1190.1190.0720.020
ine_supermercados0.0540.1090.2600.1100.3340.2630.1960.3350.4640.2700.2760.1100.1960.1050.2710.2130.1650.2500.4640.263-0.1330.2550.2190.1730.157-0.028-0.2190.8741.0000.3900.3340.2880.1780.1450.3590.2290.1670.1440.1050.273
linea20.0180.4730.7110.5711.0000.9960.6911.0001.0000.9980.9970.6260.6910.0080.9980.3950.1450.0401.0000.9960.3350.5300.1880.1580.1200.3140.3840.3820.3901.0001.0000.3660.4830.0920.8390.0830.0700.3790.6650.089
marca20.0150.5010.6030.4881.0000.9880.6011.0001.0000.9980.9980.5380.6010.0000.9980.3280.1280.0381.0000.9880.2870.4450.1630.1370.1020.2730.3290.3270.3341.0001.0000.3120.4110.0870.8390.0850.0730.3250.5590.089
pib0.1120.1210.3150.0900.3120.2750.2460.3140.4410.3390.3470.0920.2460.1170.3400.0830.3550.1980.4410.2750.1890.3820.3720.4440.381-0.094-0.1510.0580.2880.3660.3121.0000.1440.5750.3310.4930.4910.0870.0950.743
stock_disponible_total0.0190.1420.3410.2310.4110.3330.2240.4130.5710.4840.4880.2280.2240.1590.4840.154-0.0220.0000.5710.3330.1040.2920.1500.1150.0770.2020.2280.1850.1780.4830.4110.1441.0000.1000.4350.0670.028-0.1940.2270.114
tavg0.1330.0410.0600.1110.0870.0830.0500.0880.1260.0570.0570.1210.0500.0550.0570.1550.4150.5300.1260.0830.2780.0520.6860.3650.3940.2550.165-0.0960.1450.0920.0870.5750.1001.0000.0950.8520.857-0.1790.1180.659
tipo_mat20.0230.4710.7200.5770.8390.9430.6810.8471.0000.9980.9970.5490.6810.0000.9980.3280.1400.0401.0000.9430.3060.4620.1670.1420.1040.2870.3550.3510.3590.8390.8390.3310.4350.0951.0000.0860.0740.3420.6980.080
tmax0.1310.0570.0590.0900.0850.0770.0500.0870.1080.0500.0510.0970.0500.0710.0490.1210.2780.6630.1080.0770.1720.0360.6670.2230.3130.0760.010-0.0670.2290.0830.0850.4930.0670.8520.0861.0000.7650.0540.0940.559
tmin0.0770.0360.0410.0380.0730.0730.0210.0730.0820.0460.0470.0470.0210.0770.0460.1090.3640.4170.0820.0730.3120.0380.6520.2910.3390.2260.154-0.1190.1670.0700.0730.4910.0280.8570.0740.7651.000-0.1660.0450.685
tpm0.0680.1260.2900.1160.3250.2570.1830.3250.4560.2530.2590.1100.1830.0820.2540.108-0.2560.2230.4560.257-0.6800.278-0.160-0.163-0.066-0.853-0.7730.1190.1440.3790.3250.087-0.194-0.1790.3420.054-0.1661.0000.100-0.069
venta_total_neto0.0000.2920.4430.9960.5590.5260.3600.5580.7870.4900.4930.9870.3600.0370.4910.3160.0540.0450.7870.526-0.1690.3830.1180.0360.033-0.140-0.0440.0720.1050.6650.5590.0950.2270.1180.6980.0940.0450.1001.0000.162
wspd0.1360.0330.0630.1580.0890.0790.0390.0900.1080.0550.0560.1580.0390.0430.0540.1120.4810.3810.1080.0790.1530.0560.6180.3950.370-0.014-0.0160.0200.2730.0890.0890.7430.1140.6590.0800.5590.685-0.0690.1621.000

Missing values

2024-12-10T22:06:50.757993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-10T22:06:50.992806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-10T22:06:51.181769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

codigo_producto2fechastock_disponible_totalcantidadventa_total_netocosto_totalid_tipo_factura2descripcion_tipo_factura2numero_documento2cuenta_clave2centro_costo_cuenta_clave2grado2canal2canal_sap2statusstatus_2estimadoclase_sop2descripcion_producto2clasificacion2linea2categoria2marca2categoria_22codigo_activo2tipo_mat2clase_sop22clas_sop32cyber_mondayblack_fridayes_festivoee_comercioiccimacec_comercioimacec_generalimacec_no_mineroimce_comercioimce_generaline_alimentosine_supermercadostpmpibtavgtmintmaxwspd
0A1832021-02-010.00.00.000000e+000.000000e+00B1C1D1I18J18K25L3M5ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.07445.21598130.1395110.33425593.92430295.27388058.40455855.721718101.268521101.8860820.5017172.91795417.712.922.86.0
1A1832021-09-28475624000.028580000.01.077466e+212.958516e+20B24C1D204015I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010189.88590138.7632112.862324104.610354106.85197861.21495360.679626123.042606120.7632512.0016514.04051317.07.926.73.1
2A1832021-09-28475624000.028710000.01.082367e+212.971973e+20B24C1D204016I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010189.88590138.7632112.862324104.610354106.85197861.21495360.679626123.042606120.7632512.0016514.04051317.07.926.73.1
3A1832021-10-01525718000.028590000.09.720600e+204.147657e+20B24C1D204058I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107913.26.421.14.1
4A1832021-10-01525718000.028660000.09.744400e+204.157812e+20B24C1D204060I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107913.26.421.14.1
5A1832021-10-06446090000.028630000.09.161600e+204.153460e+20B24C1D204062I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN1.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107915.67.125.02.9
6A1832021-10-06446090000.028180000.09.017600e+204.088177e+20B24C1D204071I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN1.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107915.67.125.02.9
7A1832021-10-08477361000.028830000.09.225600e+204.182475e+20B24C1D204051I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107911.58.915.55.0
8A1832021-10-08477361000.029200000.09.344000e+204.236152e+20B24C1D204052I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107911.58.915.55.0
9A1832021-10-12392507000.029210000.09.347200e+204.237603e+20B24C1D204050I4J4K25L2M7ACTIVOACTIVONOI1A33B1C1D1E1F1G29H1NaNNaN0.00.00.010153.75236936.3435117.233860109.111897111.09096057.97101458.203213126.271533126.5410862.2518977.03107912.89.316.75.6
codigo_producto2fechastock_disponible_totalcantidadventa_total_netocosto_totalid_tipo_factura2descripcion_tipo_factura2numero_documento2cuenta_clave2centro_costo_cuenta_clave2grado2canal2canal_sap2statusstatus_2estimadoclase_sop2descripcion_producto2clasificacion2linea2categoria2marca2categoria_22codigo_activo2tipo_mat2clase_sop22clas_sop32cyber_mondayblack_fridayes_festivoee_comercioiccimacec_comercioimacec_generalimacec_no_mineroimce_comercioimce_generaline_alimentosine_supermercadostpmpibtavgtmintmaxwspd
5337A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI1J1K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5338A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI2J2K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5339A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5340A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI2J2K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5341A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5342A3342024-06-010.00.00.000000e+000.000000e+00B1NaNNaNI2J2K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010390.99176533.887103.418725104.912695106.85363351.48148143.78396106.419097102.6190195.7516561.7686839.84.915.10.6
5343A3612024-06-050.03000000.04.131000e+205.050473e+20B1C2D265771I18J18K50L3M5ACTIVOACTIVONOI6A143B1C2D6E6F11G133H2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5344A3342024-06-050.0200000.02.256000e+191.561253e+19B1C2D265012I1J1K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5345A3342024-06-1210539000.0200000.02.256000e+191.561253e+19B1C2D264339I1J1K39L2M2ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5346A3622024-06-190.02980000.03.278000e+203.680744e+20B1C2D265772I18J18K50L3M5ACTIVOACTIVONOI6A144B1C2D6E6F11G134H2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

codigo_producto2fechastock_disponible_totalcantidadventa_total_netocosto_totalid_tipo_factura2descripcion_tipo_factura2numero_documento2cuenta_clave2centro_costo_cuenta_clave2grado2canal2canal_sap2statusstatus_2estimadoclase_sop2descripcion_producto2clasificacion2linea2categoria2marca2categoria_22codigo_activo2tipo_mat2clase_sop22clas_sop32cyber_mondayblack_fridayes_festivoee_comercioiccimacec_comercioimacec_generalimacec_no_mineroimce_comercioimce_generaline_alimentosine_supermercadostpmpibtavgtmintmaxwspd# duplicates
77A3342023-03-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.012175.83687031.260300123.055246114.117243119.51995744.83333340.549366110.650524106.28905111.2518014.82071216.513.933.23.393
85A3342023-04-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010070.52798129.287837105.489618106.902903111.30725243.05555641.401995107.459541103.79130811.2516584.23426612.411.129.42.593
93A3342023-05-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.01.010743.04633528.401400110.444276108.001372112.63441843.95604441.355179102.95696695.31696011.2516584.23426612.24.616.61.893
101A3342023-06-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.09811.47716630.25830098.979239105.055006108.14105044.56521739.491844103.80856499.13270911.2516584.23426612.78.218.31.493
109A3342023-07-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010162.29010330.313400100.679784102.355933104.66235546.41577141.051467108.383240101.98541310.5016569.30800611.04.219.11.393
117A3342023-08-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.011169.79145428.977900105.194886107.040298110.14931946.55797143.193503104.75021497.7053239.2516569.30800612.95.821.81.193
125A3342023-09-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.010698.38610431.02450099.322139104.960454106.49177347.28682243.375785109.869264103.3002428.7516569.30800612.410.314.81.593
133A3342023-10-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.011406.97552033.116400101.734663108.096280110.47434745.60439643.914509105.285125101.2228488.7518530.78056112.78.518.84.393
141A3342023-11-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.01.011701.73391933.836500104.127872112.053880115.55072544.76744242.660888102.00659695.7051678.2518530.78056111.99.415.92.893
149A3342023-12-010.00.00.00.0B1C1NaNI1J1K39L1M1ACTIVOACTIVOOKI13A384B2C9D21E10F41G361H3J3K10.00.00.012359.99873829.709400127.096272117.385659121.11006346.48148135.678785125.748136125.9784378.5018530.78056116.411.223.15.793